The aim of this study is to develop climate-sensitive single-tree growth models, to be used in stand based prediction systems of managed forest in Switzerland. Long-term observations from ...experimental forest management trials were used, together with retrospective climate information from 1904 up to 2012. A special focus is given to the role of transformation of modelling basal area increment, helping to normalize the random error distribution. A nonlinear model formulation was used to describe the basic relation between basal area increment and diameter at breast height. This formulation was widely expanded by groups of explanatory variables, describing competition, stand development, site, stand density, thinning, mixture, and climate. The models are species-specific and contain different explanatory variables per group, being able to explain a high amount of variance (on the original scale, up to 80% in the case of Quercus spec.). Different transformations of the nonlinear relation where tested and based on the mean squared error, the square root transformation performed best. Although the residuals were homoscedastic, they were still long-tailed and not normal distributed, making robust statistics the preferred method for statistical inference. Climate is included as a nonlinear and interacting effect of temperature, precipitation and moisture, with a biological meaningful interpretation per tree species, e.g., showing better growth for Abies alba in warm and wet climates and good growing conditions for Picea abies in colder and dryer climates, being less sensitive on temperature. Furthermore, a linear increase in growth was found to be present since the 1940s. Potentially this is an effect of the increased atmospheric CO2 concentration or changed management in terms of reduced nutrient subtractions from forest ground, since industrialization lowered the demand of residue and slash uptake.
•Evaluating methods to derive stand descriptions from large-scale sampling data.•Multi-scale approach to improve initialisation of dynamic forest models.•Simultaneous parameter prediction method best ...to predict tree diameter distributions.•Random Forest approach best to predict tree species composition.
Most strategic and operational forest management decisions are taken based on stand-level information, and quantitative models of forest dynamics are key for developing sustainable management strategies. However, data on forest stands for the initialisation of such models that are representative at large spatial scales, e.g., countries or ecoregions, are often lacking. National Forest Inventories (NFIs) provide forest data from small sample plots at large spatial scales, yet deriving full stand information based on such data is challenging. Here, we evaluate seven methods of varying complexity for deriving quantitative stand descriptions based on sample data as provided by the Swiss NFI. We selected 271 extensively measured Swiss forests stands with unimodal diameter distributions, classified them as beech- vs. spruce-dominated in five development stages and randomly placed a small sized sample plot in each stand using the Swiss NFI sampling design (i.e., a circular plot of 500 m2). Seven modelling approaches were used to derive diameter distributions and species-specific stem numbers (i.e., tree species composition) from the sample data that are representative for a particular stand (local scale) and for stand types in general (generalised scale). The prediction performance of the modelling approaches was evaluated using 100 random samples per stand to calculate prediction errors. Generalised even-aged diameter distributions were best predicted by the simultaneous parameter prediction method (PPM), i.e. a combined three-step regression approach, with on average 1.3 to 2.5 times lower prediction errors compared to the simple pooling of diameter samples. However, uneven-aged diameter distributions were best predicted by pooling. At the local scale, the simultaneous PPM performed best for data from sample plots with fewer than 17 to 19 trees across all development stages. Prediction performance of the PPMs increased for structurally and spatially diverse local stands with positively skewed diameter distributions. A Random Forest approach was most suitable for predicting species composition at both the generalised and the local scale. Our study evaluates the strengths and weaknesses of methods to model stands based on data from small sample plots. We emphasise terminological pitfalls by consequently distinguishing local accuracy and generalised representativity of the stand descriptions. We demonstrate the feasibility of deriving locally accurate stands using data from small forest sample plots and evaluate the derivation of generalised stands representative at large regions. At both scales, our developments contribute to an improved initialisation of forest models and thus to a more realistic modelling of forest development under future boundary conditions.
Tree regeneration is a key process for long‐term forest dynamics, determining changes in species composition and shaping successional trajectories. While tree regeneration is a highly stochastic ...process, tree regeneration studies often cover narrow environmental gradients only, focusing on specific forest types or species in distinct regions. Thus, the larger‐scale effects of temperature, water availability, and stand structure on tree regeneration are poorly understood.
We investigated these effects in respect of tree recruitment (in‐growth) along wide environmental gradients using forest inventory data from Flanders (Belgium), northwestern Germany, and Switzerland covering more than 40 tree species. We employed generalized linear mixed models to capture the abundance of tree recruitment in response to basal area, stem density, shade casting ability of a forest stand as well as site‐specific degree‐day sum (temperature), water balance, and plant‐available water holding capacity. We grouped tree species to facilitate comparisons between species with different levels of tolerance to shade and drought.
Basal area and shade casting ability of the overstory had generally a negative impact on tree recruitment, but the effects differed between levels of shade tolerance of tree recruitment in all study regions. Recruitment rates of very shade‐tolerant species were positively affected by shade casting ability. Stem density and summer warmth (degree‐day sum) had similar effects on all tree species and successional strategies. Water‐related variables revealed a high degree of uncertainty and did not allow for general conclusions. All variables had similar effects independent of the varying diameter thresholds for tree recruitment in the different data sets.
Synthesis: Shade tolerance and stand structure are the main drivers of tree recruitment along wide environmental gradients in temperate forests. Higher temperature generally increases tree recruitment rates, but the role of water relations and drought tolerance remains uncertain for tree recruitment on cross‐regional scales.
This study presents an analysis of tree recruitment in different regions with large environmental gradients. We show that stand structure, shade tolerance. And temperature are main drivers of tree recruitment. Furthermore, the effect of water relations is subject to a high degree of uncertainty.
•Spatiotemporal tree species diversity changes are analyzed by forest inventory data.•Small-scale species richness change considered tree size and taxonomic affiliation.•Regional variation in species ...richness was mainly due to past land-use history.•Site, stand and climate affected plots-level species gains, and management losses.•Spruce was a loser, beech and fir were winners in the study period (1983–2006)
Knowledge regarding tree species dynamics is essential to understand forest responses to the environment, and to evaluate management options in adapting forest ecosystems to future climates. As maintaining tree species diversity and promoting structural stand heterogeneity are among the strategic elements in adapting forest management to climate change, the monitoring tree diversity is an ongoing challenge. Large-scale forest inventories have been proposed as a suitable basis for forest diversity analysis on large spatial and temporal scales.
We used Swiss forest inventory data (NFI) to analyse temporal changes in tree species richness on small plots from 1983 to 2006. For two size groups of trees (‘small’ trees with dbh from 12 to 35 cm from plots with 200 m2 area, and ‘large’ trees with dbh ≥ 36 cm from plots with 500 m2 area), we identified the number and the tree species appearing (‘gains’) or disappearing (‘losses’) from each plot during the study period, and related these changes to site, stand and management characteristics.
We found that species richness change was size-dependent and varied largely due to regional differences in the past land-use history of the Swiss forests. ‘Gains’ of ‘small’ trees were higher in stands with diverse vertical structure, with less competitive pressure as well as in warm environments, whereas ‘gains’ of ‘large’ trees were mostly related to climate and were highest in warm and moderately moist habitats. ‘Losses’ in both tree-size groups were mainly promoted by management. Our analysis suggests high vulnerability of Picea abies and high competitiveness of Fagus sylvatica, and underlines the potential of Abies alba in forming future Swiss forests.
Despite of the silvicultural paradigm to create more species rich forests, most silvicultural interventions decreased small-scale species richness. This calls for further studies on the effect of management on tree species diversity.
The rapid development of portable terrestrial laser scanning (TLS) devices in recent years has led to increased attention to their applicability for forest inventories, especially where direct ...measurements are very expensive or nearly impossible. However, in terms of precision and reproducibility, there are still some pending questions. In this study, we investigate the influence of stand parameters on the TLS-related visibility in forest plots. We derived 2740 stand parameters from Swiss national forest inventory sample plots. Based on these parameters, we defined virtual scenes of the forest plots with the software “Blender”. Using Blender’s ray-tracing features, we assessed the 3D coverage in a cubic space and 2D visibility properties for each of the virtual plots with different scanner placement schemes. We provide a formula to calculate the maximum number of possible hits for any object size at any distance from a scanner with any resolution. Additionally, we show that the Weibull scale parameter describing a stand, in addition to the number of trees and the mean diameter of the dominant 100 trees per hectare, has a significant and relevant influence on the visibility of the sample plot. Furthermore, we show the effectiveness and the efficiency of 40 scanner location patterns. These experiments demonstrate that intuitively distributing scanner locations evenly within the sample plot, with similar distances between locations and from the edge of the sample plot, provides the best overall visibility of the stand.
Forests provide multiple services, and in the face of global change adaptive management strategies are needed, which inevitably must be based on models. However, most locally accurate forest models ...are tied to the stand scale and cannot readily be applied across large areas. Empirical data for model initialisation are often not available at large spatial scales. National Forest Inventories (NFIs) provide spatially representative tree and stand samples, but their samples are typically small, that is, only a few trees are measured per plot, and they are truncated, that is, not each tree has the same probability of being observed. To overcome these issues, we develop and apply a methodology to derive stand descriptions from small sample data, taking the Swiss NFI as a case study.
We extended the traditional Weibull function to (multi‐)truncated unimodal and bimodal forms that are suitable for the representation of samples from survey designs with multiple callipering thresholds. Subsequently, we applied these functions in an extended parameter prediction method to derive stand diameter distributions from representative samples. Additionally, we predicted species compositions using a multinomial logistic regression model and assigned them to the diameter distributions of the stands.
The diameter distribution of 9.1% of the Swiss NFI samples was better described by a bimodal than a unimodal Weibull function. The uni‐ and bimodal diameter model in combination with the model to determine species composition can be used to predict stand descriptions from single small samples or entire forest types in the target area. Thereby, the bimodal form is suitable for capturing stand structures with distinct under‐ and overstorey. In Switzerland, the diameter distributions of stands are typically positively skewed.
Our method can be applied to any large‐scale dataset (e.g. NFI) and allows to generate initial conditions in terms of spatially representative stands. These, in turn, are suitable for forest stand simulators, which allows for developing adaptive forest management strategies at large scales, by simulating realistic and site‐specific stand development while still reflecting detailed management measures. Furthermore, stand descriptions can be used to assess tree species diversity, regeneration and harvest potentials.
Zusammenfassung
Wälder erbringen vielfältige Leistungen und im Angesicht globaler Veränderungen werden adaptive Bewirtschaftungsstrategien gebraucht, die zwangsläufig auf Modellen beruhen müssen. In der Regel sind lokal präzise Waldmodelle jedoch an die Bestandesebene gebunden und lassen sich nicht direkt auf großräumige Regionen übertragen. Empirische Daten zur Modellinitialisierung sind auf großen räumlichen Skalen oft nicht verfügbar. Nationale Waldinventuren (NFIs) liefern räumlich repräsentative Baum‐ und Bestandesstichproben, diese sind aber typischerweise klein (nur wenige Bäume pro Aufnahmefläche) und trunkiert (nicht jeder Baum hat dieselbe Aufnahmewahrscheinlichkeit). Um diese Lücke zu füllen, entwickeln wir eine Methodik zur Herleitung von Bestandesbeschreibungen aus kleinen Stichprobendaten und zeigen am Beispiel des Schweizerischen Landesforestinventars (NFI) eine konkrete Anwendung.
Wir erweiterten die Weibull‐Funktion zu einer (multi‐)trunkierten unimodalen und bimodalen Form, die sich für die Beschreibung von Stichproben aus Aufnahmedesigns mit mehreren Kluppschwellen eignen. Anschließend wendeten wir diese Funktionen in einer simultanen Parameter‐Schätzmethode über alle Stichproben an, um repräsentative Bestandes‐Durchmesserverteilungen abzuleiten. Zusätzlich prognostizierten wir die Baumartenzusammensetzung mit Hilfe eines multinomialen logistischen Regressionsmodells und verbanden diese mit den Durchmesserverteilungen der Bestände.
Die Durchmesserverteilung von 9,1% der Schweizerischen NFI Stichproben wurde besser durch eine bimodale als durch eine unimodale Weibull‐Funktion beschrieben. Das uni‐ und bimodale Durchmessermodell in Kombination mit dem Modell zur Bestimmung der Baumartenzusammensetzung kann zur Vorhersage von Bestandesbeschreibungen aus einzelnen kleinen Stichproben oder ganzen Waldtypen im Zielgebiet verwendet werden. Dabei eignet sich die bimodale Form zur Erfassung von Bestandesstrukturen mit ausgeprägtem Unter‐ und Oberstand. In der Schweiz sind die Durchmesserverteilungen von Beständen typischerweise rechtsschief.
Unsere Methode kann auf jeglichen grossräumigen Datensatz (z.B. NFI) angewendet werden und erlaubt es, Initialbedingungen in Form von räumlich repräsentativen Beständen zu generieren. Diese wiederum können in Waldbestandessimulatoren verwendet werden, um die Entwicklung von adaptiven Waldbewirtschaftungsstrategien auf großräumigen Skalen zu untersuchen, indem realistische und standortspezifische Bestandesentwicklungen mit zielgenauer Bewirtschaftung simuliert werden. Darüber hinaus können die Bestandesbeschreibungen zur Beurteilung der Baumartenvielfalt, der Verjüngung und des Erntepotentials genutzt werden.
•Equations were based on rates of change rather than state variables.•Self-thinning equations accounted for species mixing, dominance, site, and climate.•All-species equations were also developed, ...and could be used for rarer species.•Based on temperate, Mediterranean and subtropical stands in Australia, China and Switzerland (1579 plots).
Self-thinning dynamics are often considered when managing stand density in forests and are used to constrain forest growth models. However, self-thinning relationships are often quantified using only data at a conceptualised self-thinning line, even though self-thinning can begin before the stand actually reaches a self-thinning line. Also, few self-thinning relationships account for the effects of species composition in mixed-species forests, and stand structure such as relative height of species (in mixtures), and/or size or age cohorts in uneven-aged forests. Such considerations may be important given the effects of global climate change and interest in mixed-species and uneven-aged forests.
The objective of this study was to develop self-thinning relationships based on changes in the tree density relative to mean tree diameter, instead of focusing only on data for state variables (e.g. tree density) at the self-thinning line. This was done while also considering how the change in tree density is influenced by site quality and stand structure (species composition and relative height).
The relationships were modelled using data from temperate Australian Eucalyptus plantations (436 plots), subtropical forests in China (88 plots), and temperate forests in Switzerland (1055 plots). Zero-inflated and hurdle generalized linear models with Poisson and negative binomial distributions were fit for several species, as well as for all-species equations.
The intercepts and slopes of the self-thinning lines were higher than many published studies which may have resulted from both the less restrictive equation form and data selection. The rates of self-thinning often decreased as the proportion of the object species increased, as relative height increased (species or size cohort became more dominant), and as site (quality) index increased. The effects of aridity varied between species, with self-thinning increasing with aridity index for Abies alba, Pinus sylvestris, Quercus petraea and Quercus robur, but decreasing with aridity index for Eucalyptus nitens, Fagus sylvatica and Picea abies as sites became wetter and cooler. Self-thinning model parameters were not correlated with species traits, including specific leaf area, wood basic density or crown diameter – stem diameter allometry. All-species self-thinning relationships based on all data could be adjusted using a correction factor for rarer species where there were insufficient data to develop species-specific equations.
The approach and equations developed could be used in forest growth models to calculate how the tree density declines as mean tree size increases, as height changes relative to other cohorts or species, as species proportions change, and as climatic and edaphic conditions change.
Sustainable forest management plays a key role for forest biodiversity and the provisioning of ecosystem services (BES), including the important service of carbon sequestration for climate change ...mitigation. Forest managers, however, find themselves in the increasingly complex planning situation to balance the often conflicting demands in BES. To cope with this situation, a prototype of a decision support system (DSS) for strategic (long-term) planning at the forest enterprise level was developed in the present project. The DSS was applied at three case study enterprises (CSEs) in Northern Switzerland, two lowland and one higher-elevation enterprise, for a 50-year time horizon (2010 to 2060) under present climate and three climate change scenarios (‘wet’, ‘medium’, ‘dry’). BES provisioning (for biodiversity, timber production, recreation, protection against gravitational hazards and carbon sequestration) was evaluated for four management scenarios (no management, current (BAU), lower and higher management intensity) using a utility-based multi-criteria decision analysis. Additionally, four alternative preference scenarios for BES provisioning were investigated to evaluate the robustness of the results to shifting BES preferences. At all CSEs, synergies between carbon sequestration, biodiversity and protection function as well as trade-offs between carbon sequestration and timber production occurred. The BAU management resulted in the highest overall utility in 2060 for different climate and BES preference scenarios, with the exception of one lowland CSE under current BES preference, where a lower intensity management performed best. Although climate change had a relatively small effect on overall utility, individual BES indicators showed a negative climate change impact for the lowland CSEs and a positive effect for the higher elevation CSE. The patterns of overall utility were relatively stable to shifts in BES preferences, with exception of a shift toward a preference for carbon sequestration. Overall, the study demonstrates the potential of the DSS to investigate the development of multiple BES as well as their synergies and trade-offs for a set of lowland and mountainous forest enterprises. The new system incorporates a wide set of BES indicators, a strong empirical foundation and a flexible multi-criteria decision analysis, enabling stakeholders to take scientifically well-founded decisions under changing climatic conditions and political goals.
To understand the state and trends in biodiversity beyond the scope of monitoring programs, biodiversity indicators must be comparable across inventories. Species richness (SR) is one of the most ...widely used biodiversity indicators. However, as SR increases with the size of the area sampled, inventories using different plot sizes are hardly comparable. This study aims at producing a methodological framework that enables SR comparisons across plot‐based inventories with differing plot sizes. We used National Forest Inventory (NFI) data from Norway, Slovakia, Spain, and Switzerland to build sample‐based rarefaction curves by randomly incrementally aggregating plots, representing the relationship between SR and sampled area. As aggregated plots can be far apart and subject to different environmental conditions, we estimated the amount of environmental heterogeneity (EH) introduced in the aggregation process. By correcting for this EH, we produced adjusted rarefaction curves mimicking the sampling of environmentally homogeneous forest stands, thus reducing the effect of plot size and enabling reliable SR comparisons between inventories. Models were built using the Conway–Maxell–Poisson distribution to account for the underdispersed SR data. Our method successfully corrected for the EH introduced during the aggregation process in all countries, with better performances in Norway and Switzerland. We further found that SR comparisons across countries based on the country‐specific NFI plot sizes are misleading, and that our approach offers an opportunity to harmonize pan‐European SR monitoring. Our method provides reliable and comparable SR estimates for inventories that use different plot sizes. Our approach can be applied to any plot‐based inventory and count data other than SR, thus allowing a more comprehensive assessment of biodiversity across various scales and ecosystems.
Species richness is a commonly used biodiversity indicator. Because it strongly depends on the area sampled, comparability of species richness estimates across inventories using different plot sizes is not directly possible. We provide a methodological framework based on rarefaction curves to tackle this challenge.
The commitment to report greenhouse gas emissions requires an estimation of biomass stocks and their changes in forests. When this was first done, representative biomass functions for most common ...tree species were very often not available. In Germany, an estimation method based on solid volume was developed (expansion procedure). It is easy to apply because the required information is available for nearly all relevant tree species. However, the distributions of neither parameters nor prediction intervals are available. In this study, two different methods to estimate above-ground biomass for Norway spruce (Picea abies), European beech (Fagus sylvatica), and Scots pine (Pinus sylvestris) are compared. First, an approach based on information from the literature was used to predict above-ground biomass. It is basically the same method used in greenhouse gas reporting in Germany and was applied with prior and posterior parameters. Second, equations for direct estimation of biomass with standard regression techniques were developed. A sample of above-ground biomass of trees was measured in campaigns conducted previously to the third National Forest Inventory in Germany (2012). The data permitted the application of Bayesian calibration (BC) to estimate posterior distribution of the parameters for the expansion procedure. Moreover, BC enables the calculation of prediction intervals which are necessary for error estimations required for reporting. The two methods are compared with regard to predictive accuracy via cross-validation, under varying sample sizes. Our findings show that BC of the expansion procedure performs better, especially when sample size is small. We therefore encourage the use of existing knowledge together with small samples of observed biomass (e.g., for rare tree species) to gain predictive accuracy in biomass estimation.
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